1. Field
The present invention relates to adaptive filtering and, more specifically, to an adaptive filtering system and method for reducing interference in a medical diagnostic signal.
2. Discussion of the Related Art
Medical monitoring equipment is often used in relatively xe2x80x9cnoisyxe2x80x9d environments. Electrical noise can corrupt signals processed by the medical monitoring equipment. For example, signals from an electrocardiogram (ECG or EKG) are often used by medical professionals to diagnose heart disorders or ailments. The ECG signals often are corrupted by electrical noise from adjacent electrical supply and power lines in the vicinity of where the equipment is being used. In particular, because the desired electrical signals in an ECG are very low voltage, they are highly susceptible to noise introduced from different sources including electrical supply and power lines.
Because a normal ECG signal has frequency components which are the same frequency as common power lines, for example, interference from the power lines which occurs on the ECG signal can be difficult to separate from the desired ECG signal.
To reduce the noise in a desired ECG signal, narrow band notch filters are often used. However, the use of narrow band notch filters is of limited utility. For example, on one hand, such a notch filter must be very selective, having a very thin bandwidth in order to avoid filtering any of the desired components of the ECG signal. This requires a high order filter which requires many components or a relatively lengthy processing time. In addition, the frequency of a power line signal typically varies somewhere around 60 Hz (50 Hz in Europe) and accordingly, the noise component may vary. Accordingly, on the other hand, a narrow band filter having a fixed notch frequency has to be relatively broad banded to reduce the noise induced by the power line. In addition, although the power line frequency in the United States is relatively fixed and stable at 60 Hz, the power line frequency in European countries can vary substantially from country to country and even from day to day in some countries. Accordingly, fixed narrow band filtering techniques are of limited utility.
Another type of filtering technique is an adaptive filtering technique. An example of an adaptive filter is shown in FIG. 1A. A reference signal r is correlated to the noise and is filtered by a finite impulse response (FIR) filter 10. Filter 10 has adaptive parameters which very the frequency of the signal being filtered based on feedback signal f. The filtered noise correlated reference signal r is then subtracted from the input signal s. Using this technique, the feedback adjusts the parameters of filter 10 so that undesired noise in the output signal is minimized. In this way, a filter with large stopband damping can be provided with minimal damping in the passband. Furthermore the frequency of the noise makes no difference, so that adaptive filtering finds some use in those countries where power line frequency varies considerably.
Various algorithms can be used to update or adjust the parameters of filter 10. One simple method is referred to as minimizing the least mean square (LMS) of the output. To achieve this, filter parameters can be updated in the following way:
Wn+1=Wn+xcexcy(n)r*(n) (* designates complex conjugate)xe2x80x83xe2x80x83(1)
where Wn, is the parameter array at sample number n and xcexc is the step size used to determine how fast the filter adapts.
In the adaptive filter shown in FIG. 1A, the purpose is to filter the noise correlated reference signal r so that it resembles the noise portion of the ECG+noise signal s. Then, by subtracting the filtered noise correlated reference signal rxe2x80x2 from the ECG+noise signal s, effective cancellation of the noise can be achieved. Although such an adaptive filtering technique may be effective for certain applications, such technique is not ideal and difficulties still remain.
An ECG output signal is shown in FIG. 1B. An ECG waveform for a single beat is typically referred to as a PQRST complex. At initiation of a heart beat, a P wave appears. This corresponds to activity in the artia. The QRST complex then follows the P wave. The QRST complex corresponds to ventricular activity. More specifically, it is generally understood that the QRS portion of the wave corresponds to electrical activation of the ventricles and the T wave corresponds to their electrical recovery. The ST segment is relatively quiescent.
A typical ECG signal contains a 60 Hz component during a limited period of time. That is, a 60 Hz component is present in the QRS complex. During this short period the reference signal is highly correlated with the pure ECG signal. The adaptive filter coefficients are updated incorrectly. This results in a 60 HZ disturbance in the ST-segment before the coefficients readjust. Accordingly, if a filter is used to remove 60 Hz noise, a portion of the actual ECG signal itself is removed, thus an error occurs when using filter 10, resulting in disturbance in the desired ECG signal. For example, FIG. 2A shows an ECG signal with simulated 50 Hz power line interference added. FIG. 2B shows the result of using filter 10 on the signal shown in FIG. 2A. FIG. 2C shows a closer look at a ST-segment of the filtered signal, the ST-segment being corrupted by 50 Hz interference.
Accordingly, a need exists for a efficient system for eleminating noise in an ECG signal.
A signal filter includes a first adaptive filter receiving a desired signal containing a noise component and a first noise correlated reference signal, and outputting a first filtered output signal. A second adaptive filter, receives the desired signal containing the noise component and a second noise correlated reference signal and outputs a second filtered output signal. Signal filter parameters of the second adaptive filter are controlled based on the first filtered output signal and the second filtered output signal. The first and second noise correlated reference signals may be the same. The signal filter parameters of the first adaptive filter are adapted according to the formula W1,n+1=W1,n+xcexcy1(n)r*(n), where n is the sample number and xcexc is the step size. The signal filter parameters of the second adaptive filter are adapted according to the formula W2,n+1=W2,n+xcex7(n)xcexcy2(n)r*(n), where n is the sample number, xcexc is the step size and xcex7 is a weighted signal based on the first filtered output signal. The first adaptive filter may include an adder, a finite impulse response (FIR) filter and an adaptive processor for controlling the adaptive parameters of the FIR filter. The second adaptive filter may include an adder, a second finite impulse response (FIR) filter and a second adaptive processor for controlling the adaptive parameters of the second FIR filter. A mathematical operation unit can be provided for outputting a weighted output signal based on the first filtered output signal, the weighted output signal being provided to the second adaptive filter as the first filtered output signal.
According to another aspect, a signal filtering method includes receiving a desired signal containing a noise component and receiving a noise correlated reference signal. Filtering of the noise correlated reference signal is performed using a first set of adaptive parameters to provide a first filtered noise correlated reference signal. The first filtered noise correlated reference signal is subtracted from the desired signal containing the noise component to provide a first output signal. The first set of adaptive parameters are controlled based on the first output signal. Filtering of the noise correlated reference signal is performed using a second set of adaptive parameters and a signal corresponding to the first output signal to provide a second filtered noise correlated reference signal and the second filtered noise correlated reference signal is subtracted from the desired signal containing the noise component to provide a second output signal, the second output signal being the desired signal with a minimized noise component. The second set of adaptive parameters is controlled based on the second output signal. The first adaptive parameters may be adapted according to the formula W1,n+1=W1,n+xcexcy1(n)r*(n), where n is the sample number and xcexc is the step size. The second adaptive parameters may be adapted according to the formula W2,n+1=W2,n+xcex7(n)xcexcy2(n)r*(n), where n is the sample number, xcexc is the step size and xcex7 is a weighted signal based on the first output signal. The signal filtering method may further include a step of performing a mathematical operation on the first output signal to provide a weighted output signal as the signal corresponding to the first output signal.
According to another aspect, a signal filtering system includes a coarse filter including a subtractor for subtracting a first filtered noise correlated reference signal from a desired signal containing a noise component and providing a first output signal, a filter for receiving and filtering a noise correlated reference signal using a first set of adaptive parameters and outputting the first filtered noise correlated reference signal, and an adaptive processor for adaptively processing the adaptive parameters based on the first output signal. The system also includes a signal filter including a subtractor for subtracting a second filtered noise correlated reference signal from the desired signal containing the noise component and providing a second output signal, a filter for receiving and filtering the noise correlated reference signal using a second set of adaptive parameters and information corresponding to the first output signal and outputting the second filtered noise correlated reference signal, and an adaptive processor for adaptively processing the second adaptive parameters based on the second output signal. The system may further include a mathematical operation unit for performing a mathematical operation on the first output signal and providing an output to the signal filter as the information corresponding to the first output signal. The second output signal may include the desired signal with a minimized noise component. The filters can be finite impulse response filters.
According to another aspect, a signal filter includes a filter receiving a noise correlated reference signal and outputting a filtered noise correlated reference signal filtered based on a set of parameters and a weighted signal. A subtractor subtracts the filtered noise correlated reference signal from a desired signal including a noise component and outputs an output signal. A processor modifies the set of parameters based on the output signal. The signal filter may further include a second filter receiving the noise correlated reference signal and outputting a second filtered noise correlated reference signal based on a second set of parameters. A subtractor subtracts the second filtered noise correlated reference signal from the desired signal including a noise component and outputs a second output signal. A processor modifies the second set of parameters based on the second output signal. The signal filter may further include a mathematical operation unit receiving the second output and outputting the weighted signal.
Methods are also described for determining the noise correlated reference signal.